Data Centralisation: Chapter 4

Siloed data, which exists across different departments and systems, can lead to data duplication and data hygiene issues.

As Navin Persaud, VP of Revenue Operations at 1Password, said:

"The problem in many of the go-to-market stacks today is that a lot of data is siloed and has to be transformed so that it can be used and then stored in a place where it can be queried."

"A lot of companies haven’t figured that out. They don’t have the pipelines, the structure, or the process alignment to capture, cleanse and normalise data, and as a result, you have a lot of people thinking different things because their data is telling them different things."

That’s why managing your data in a centralised repository is key. Centralised databases help create a unified data strategy for all go-to-market teams and maintain a single source of truth across your organisation.

The benefits of data centralisation

• Data centralisation helps teams make data-informed decisions and analyse and evaluate key strategies and initiatives.
• Centralising your data helps prevent data silos and create a unified funnel by integrating all information into a single and shareable resource. This also allows businesses to maintain data integrity and improve data accuracy and hygiene.
• Up-to-date data allows teams to create more complete customer profiles and comprehend their audiences better.

Jeff Ignacio highlighted the importance of this point. He says:

"Much of the data is housed in your CRM, or for the marketing organisation, it will be in their marketing automation platform. Your customer usage metrics will be sitting in some sort of product database, and all of these data points can be combined and used to answer the question, ‘What is happening to this customer from a 360-degree view?"

• This holistic view of customer data enables organisations to generate relevant and refined customer segments and provide personalised experiences and satisfactory customer experiences.
• Establishing a customer data platform can also help track customer insights, predict and prevent churn and identify opportunities for upsells and cross-sells.
• Complete and accurate data can help optimise reporting processes and data storytelling.
• Bad data can compromise your understanding of your ICP. With consolidated and enriched data, organisations can identify who to target and which customer segments or channels will be the most profitable. That way, businesses can optimise their resources accordingly.
• This ensures teams focus on the highest quality leads and improving conversion rates.

How to centralise your data

1. Identify all your data sources 

Start by identifying all the sources of B2B data within your organisation. Businesses must connect disparate data sources from various departments, including sales, marketing, and customer support.

These sources may include CRM systems, marketing automation platforms, customer support software, financial tools, and more.

This is a crucial step; as Navin said:

"The data team’s responsibility is around curating the data from many system sources and from all areas of product, marketing, and financial systems so that there’s a playground for the rest of us to leverage and use."

"It democratises data in a single place for us to look at and understand where we want to pull trends or insights or analysis from."

Create a comprehensive list of data sources to clearly understand what needs to be centralised into one single location.

2. Map out your data architecture and warehouse

This means storing and organising data in a structured manner and making it easily accessible for analysis.

Cloud data warehouses have become increasingly popular due to their scalability, flexibility, and cost-effectiveness. 

By having a centralised data infrastructure, teams can perform cross-functional analyses and gain valuable insights.

3. Choose the right centralisation tool

Your centralised data platform should align with your organisation’s needs, budget, and technical capabilities.

4. Plan how to clean and enrich the data

Data quality and enrichment are important, so establishing data quality checks and validation rules is key.

If you miss this step, you risk unactionable data, leads that aren’t properly followed up on, and revenue loss.

Data enrichment is the process that refines and strengthens the value of the data in your CRM. It helps identify, update and enhance any missing information in your CRM from multiple internal and external sources.

5. Outline how data will flow from source to destination 

This starts with determining how you’ll extract the data from your sources. It may involve APIs, CSV exports, or other data export methods, batch processing, or real-time data streaming.

You need a good capture system to trap revenue data that could be missing from your core systems. This can be aided by simplifying your tech stacks and resolving technology disconnects.

6. Make sure your data is comprehensible

Good data visualisation is an essential part of data utilisation, allowing employees to quickly find trends, measurements, and accurate insights.

Everyone should be able to look at your data insights and understand the patterns they’re showing. This means training everyone in your organisation to be able to analyse data in real-time so they can form predictions about what’s coming next.

Flexibility is also crucial; whether you use a business intelligence platform or build in-house dashboards, employees need to be able to dissect data with multiple filters and measure different variables.

When marketing can see sales interactions with a customer and vice versa, they can better communicate with one another to devise the next steps for moving them along the pipeline.

7. Create advanced reports and dashboards

This involves using business intelligence tools to extract actionable insights from the data.

Dashboards and reports can provide a real-time view of key performance indicators, helping RevOps teams identify opportunities and challenges quickly.

8. Create clear data governance policies and assign responsibility for data management tasks

Data governance is a set of practices and policies that prevents poor data quality and ensures security, and compliance.

Your data architecture should include mechanisms to enforce data governance, ensuring that the data used for decision-making is clean, accurate and compliant with industry regulations.

Define access controls, data ownership, and data usage guidelines.

9. Monitor your data centralisation pipeline

Look for issues and performance bottlenecks.

Implement data monitoring tools and review data quality reports to ensure everything runs smoothly.

Working with a lot of data can make decision processes tricky. To combat this, you could also collaborate with executives to develop a data decision framework.

This will inform team members on whether or not to move a prospect to a closing stage or continue with nurturing, all based on data.

Closing thoughts

With centralised data, businesses gain a holistic view of their customers, allowing them to personalise their marketing efforts and provide a seamless customer experience.

They can analyse customer interactions across various touchpoints and channels, identifying patterns and trends that can inform their marketing strategies.

By following these steps, businesses can drive revenue growth and improve data hygiene and accuracy.